Anomaly Detection and Early Warning Model for Latency in Private 5G Networks

Abstract

Different from previous generations of communication technology, 5G has tailored several modes especially for industrial applications, such as Ultra-Reliable Low-Latency Communications (URLLC) and Massive Machine Type Communications (mMTC). The industrial private 5G networks require high performance of latency, bandwidth, and reliability, while the deployment environment is usually complicated, causing network problems difficult to identify. This poses a challenge to the operation and maintenance (O&M) of private 5G networks. It is needed to quickly diagnose or predict faults based on high-dimensional data of networks and services to reduce the impact of network faults on services. This paper proposes a ConvAE-Latency model for anomaly detection, which enhances the correlation between target indicators and hidden features by multi-target learning. Meanwhile, transfer learning is applied for anomaly prediction in the proposed LstmAE-TL model to solve the problem of unbalanced samples. Based on the China Telecom data platform, the proposed models are deployed and tested in an Automated Guided Vehicles (AGVs) application scenario. The results have been improved compared to existing research.

Federated and Transfer Learning Applications

“…Anomaly detection for latency in 5G networks: Han et al [7] proposed a novel approach for detecting anomalies and issuing early warnings with application to abnormal driving scenarios involving Automated Guided Vehicles (AGVs) within the private 5G networks of China Telecom. This approach efficiently identifies high-latency cases through their proposed ConvAE-Latency model.…”

“…With this in mind, the present Special Issue of Applied Sciences on "Federated and Transfer Learning Applications" provides an overview of the latest developments in this field. Twenty-four papers were submitted to this Special Issue, and eleven papers [1][2][3][4][5][6][7][8][9][10][11] were accepted (i.e., a 45.8% acceptance rate). The presented papers explore innovative trends of federated learning approaches that enable technological breakthroughs in highimpact areas such as aggregation algorithms, effective training, cluster analysis, incentive mechanisms, influence study of unreliable participants and security/privacy issues, as well as innovative breakthroughs in transfer learning such as Arabic handwriting recognition, literature-based drug-drug interaction, anomaly detection, and chat-based social engineering attack recognition.…”

Anomaly Detection through Grouping of SMD Machine Sounds Using Hierarchical Clustering

“…There is a need for a system that can immediately detect abnormalities [1,2] when there is a problem in the manufacturing line; as products become linearly diversified, there is a need for a method that can effectively detect abnormalities in products. Considering the real-world scenario, where a sufficient amount of abnormal data cannot be obtained, unsupervised learning, which trains only the normal data with the autoencoder [3][4][5] and detects the anomaly with the reconstruction error [6], is suitable.…”

“…The proposed method is an anomaly detection technique based on the hierarchical clustering of latent vectors obtained through a pre-trained autoencoder. The autoencoder [3][4][5] is trained to represent data well in the latent space through the compression and reconstruction, and if the latent vectors obtained through the encoder of the pre-trained autoencoder have similar characteristics, the distances in the latent space will be closer than the data with different characteristics. Hierarchical clustering [10] is a system that groups objects into clusters based on their distance from individual clusters.…”